• Interpretative language (no compilation needed)
• No manual memory management
• Vectorized

• Widely used, vast community of R users
• Good life expectancy

• Open-source: anyone can see/create/modify
• Multiplatform: Windows, Mac, Unix… It works everywhere

• More and more packages
• More and more popular among data scientists and (now) biologists

• vector: Ordered collection of elements of the same type

• list: Flexible container, mixed type possible. Recursive

• matrix: Table of elements of the same type

• data.frame: Table of mixed type elements

Try to raise errors using operators.

• c() Concatenate function
• 1:10 Vector with numbers from 1 to 10

• c() Concatenate function
• 1:10 Vector with numbers from 1 to 10

• Create a vector with 7 numeric values
• Create a vector with 7 character values

Using index/position between []

• length() Number of elements in the vector
• names() Get or set the names of the vector’s value

• sort() Sort a vector
• sample() Shuffle a vector
• rev() Reverse a vector

• head()/tail() Print the first/last values
• summary() Summary statistics
• min()/max()/mean()/median()/var() Minimum, maximum, average, median, variance
• sum Sum of the vector’s values

• Simple arithmetic operations over all the values of the vector
• Or values by values when using vectors of same length
• Arithmetic operations: +, -, *, /
• Other exist but let’s forget about them for now

• There are three basic types of objects: numeric, character and boolean
• Basic objects can be combined into more complex objects, such as vectors, lists, matrices and data.frames
• Objects need to be ‘stored’ in variables so they can be easily retrieved and modified
• Basic and complex numeric objects can handle arithmetic operations
• Functions can be applied to basic and/or complex objects depending on the type of data they are designed to process

• A vector is a sequence of elements that are all of the same type
• There are multiple ways to create it
• [] are used to manipulate elements of a vector
• Numeric vectors support arithmetic operations
• Elements in a vector can be named
• Various functions can be used with vectors to modify or summarize them

• Apply a function to each row (or column) of a matrix
• No manual iteration, the loop is implicit
• Second parameter: 1 means row and 2 means columns

To tell R where to find your data, you need to specify the path to the file. There are two types of paths for the same file:

• Absolute Path: This is the full path to the file, which you can see in the address bar when you are in a folder.
• Relative Path: This describes how to navigate from one folder to another to reach the file.

Paths are always enclosed in double quotes (").

Change the directory where R is working

``

• What data structure is more appropriate? vector, matrix?
• Does R read/write the file the way you want?
• The extra parameters of the functions are your allies

To read a data.frame from a multi-column file

• file=the path to the file
• header= Set to TRUE if the 1st line correspond to column names
• as.is= Set to TRUE to read the values as simple type, recommended
• sep= The character that separate the columns, e.g. , or \t (tabs)
• row.names= The column number to use as row names ``

To write a data.frame in a multi-column file

• df the data.frame to write
• file= the file name
• col.names= TRUE print the column names in the first line
• row.names= TRUE print the row names in the first columns
• quote= TRUE surround character by double quotes
• sep= the character that separates each column. ’ ’ by default.

• A matrix is a table containing elements of one data type
• A data.frame is a table with columns of different data types
• matrix(), data.frame(): functions to create a matrix or a data frame
• [i,j]: used to manipulate and replace elements
• rownames(), colnames(): get/set the row or column names
• Numeric columns support arithmetic operations
• apply(): run a function on each row or column

• Set your working directory, eg. the folder where you are working (Session > Set Working Directory)
• read.table(): Basic function to read a table
• write.table(): Basic function to write a table
• pdf()/png()/jpeg() ... dev.off(): Save a plot in PDF/PNG/JPEG format automatically
• You can save R objects/environments (saveRDS(), save(), save.image())
• You can load R objects/environments (readRDS(), load())

Plot a histogram, eg. the value distribution of a vector.

x The vector with the values to plot

Plot a barplot, eg. one bar for each value of a vector.

x The vector with the values to plot

Plot one vector against the other using points for each element.

x The first vector to plot (x-axis)

y The second vector to plot (y-axis)

type How the points are plotted. “p” for points, “l” for points joined by lines

Plot the distribution of variables.

x The matrix of distributions

col= The colour of the points/lines

pch= The shape of the points

lty= The shape of the lines

lines() Same as plot but super-imposed to the existent one abline() Draw a vertical/horizontal line

TRUE / FALSE values

Test a condition. If TRUE, runs some instructions. If FALSE, runs something else or nothing.

# example 1
if( Condition ){
... Instructions
} else {
... Instructions
}
# example 2
ifelse(condition, instruction if TRUE, instruction if FALSE)

Write a if block that automatically classify the expression of the first gene of mat.ge into:

• ‘high’ if its maximum value is higher than 4
• ‘low’ if not

Create a function that classify the average value of a vector. It returns:

• small if the average is below 3
• medium if the average is between 3 and 7
• high if the average is above 7

Test your function on vectors with random numbers from 0 to 10.

• Create a function that returns the average of the minimum and maximum value of a vector
• Create a function that returns how many values are higher than 3 in a vector
• Use your functions on all mat.gene data frame to get the minimum and maximum value of each gene and how many genes are higher than 3 in each sample

Test your functions on vectors with random numbers from 0 to 10.

Iterates over the elements of an iterator and runs intructions.

for(v in vec){
... Instruction
}

Runs instructions as long as a condition is TRUE.

while( CONDITION ){
... Instruction
}

Write a function that computes the mean values of the columns:

• using the apply function
• using a for loop
• using a while loop